Generating an image mask using machine learning

ABSTRACT

A machine learning system can generate an image mask (e.g., a pixel mask) comprising pixel assignments for pixels. The pixels can be assigned to classes, including, for example, face, clothes, body skin, or hair. The machine learning system can be implemented using a convolutional neural network that is configured to execute efficiently on computing devices having limited resources, such as mobile phones. The pixel mask can be used to more accurately display video effects interacting with a user or subject depicted in the image.

RELATED APPLICATIONS

This application is a continuation of and claims the priority of benefitof U.S. patent application Ser. No. 15/706,057, filed on Sep. 15, 2017,which claims the priority of benefit of U.S. Provisional Application No.62/481,415, filed on Apr. 4, 2017, which is hereby incorporated byreference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to imageprocessing and, more particularly, but not by way of limitation, togenerating image masks using machine learning.

BACKGROUND

In recent years, mobile computing devices have allowed users to applyimage effects (e.g., image overlays, video effects) to one or moreimages captured via the client devices. Image effects can be applied toregions within a given image (e.g., recolor pixels of a person's facewhile leaving pixels of the person's hair unmodified). However, labelingdifferent regions of a given image can be computationally intensive,especially on mobile devices which can be limited processing power andmemory.

BRIEF DESCRIPTION OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure (“FIG.”) number in which that element or act is first introduced.

FIG. 1 is a block diagram showing an example messaging system forexchanging data (e.g., messages and associated content) over a network.

FIG. 2 is block diagram illustrating further details regarding amessaging system having an integrated virtual object machine learningsystem, according to example embodiments.

FIG. 3 is a schematic diagram illustrating data that may be stored in adatabase of a messaging server system, according to certain exampleembodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to content (e.g., an ephemeralmessage, and associated multimedia payload of data) or a contentcollection (e.g., an ephemeral message story) may be time-limited (e.g.,made ephemeral).

FIG. 6 illustrates a block diagram showing example functional componentsin an image mask system, according to some embodiments.

FIG. 7 shows a flow diagram of a method for implementing labeling pixelswith mask data using an image mask system, according to some exampleembodiments.

FIG. 8 shows a functional architecture for processing label data for amask, according to some example embodiments.

FIG. 9 displays example architecture for the segmentation engine,according to some example embodiments.

FIG. 10 shows an example architecture of a bottle module, according tosome example embodiments.

FIG. 11 shows an example of processing using different sized image setsof one or more images, according to some example embodiments.

FIG. 12 shows an example architecture of a post-processing engine,according to some example embodiments.

FIG. 13 shows an example architecture for efficient post-processing,according to some example embodiments.

FIG. 14 shows an example input image of a labeled subject, according tosome example embodiments.

FIG. 15 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 16 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

In recent years, mobile computing devices have allowed users to applyimage effects (e.g., image overlays, video effects) to one or moreimages captured via the client devices. Image effects can be applied toregions within a given image using image masks. An image mask is arepresentation of a given image that labels areas within the image.Image masks can, for example, be used to recolor pixels of a person'sface while leaving pixels of the person's hair unmodified. However,generating image masks for different regions of a given image can becomputationally intensive, especially on mobile devices which can belimited processing power and memory.

To this end, an image mask can be used to label different areas of animage to different values. For example, a face image mask may leavepixels of the face unmodified, but may set values outside the face areato zero (e.g., where a pixel of zero value is black). Mask data arelabels, which can be stored in a different image file. The image maskfile may be an image that has the same pixel dimensions as the originalimage, but each pixel may be set to a given value to indicate the areaor label to which it belongs. In some example embodiments, mask data(e.g., pixel values for labels) can be stored as channel data of eachpixel (e.g., instead of a pixel having a 3-channel RGB value, it mayhave a fourth M channel, e.g., RGBM). Further, the image mask data maybe polygon metadata, whereby each polygon encircles a given type ofmasked area, and the polygon vertices are stored as metadata for a givenimage. Using image masks, image and video affects can be applied todifferent areas of an image with greater specificity.

In some example embodiments, a set of training images are labeled withpolygons around each area (e.g., face area, hair area, clothes area).The polygons can be created using human labels (e.g., that drag and holdover different areas or outline different areas to create the polygons).Vertices of the polygons can be stored as label metadata.

The labeled set of training images (e.g., image masks) can be resizedinto sets of training images of different sizes, e.g., large, medium,small. A segmentation engine implementing a neural network can train itsneural network model using the different sized training image sets aspart of a multi-scale training process. The training process configuresthe neural network model to receive a given original image as an inputand output an image mask with different areas labeled into segments.After training, the neural network can run from client devices that useimages of different resolutions, as discussed in further detail below.Further, in some example embodiments, generated image masks can berefined in a post-processing phase to refine borders of the labeledareas and remove noise.

At runtime, the user can use his or her client device to take one ormore images (e.g., a photograph, a video sequence). The trained imagemask system can then efficiently detect the different areas and labelthem as different segments in an image mask. Different visual effectscan be applied to differently labeled areas. For example, if the maskincludes a hat area, a depicted hat may be replaced with a zany cartoonhat. The modified images (e.g., an image of a subject wearing a zanyhat) can be published as an ephemeral message on a social media networkdirectly from the client device, according to some example embodiments.

FIG. 1 is a block diagram showing an example messaging system 100 forexchanging data (e.g., messages and associated content) over a network.The messaging system 100 includes multiple client devices 102, each ofwhich hosts a number of applications including a messaging clientapplication 104. Each messaging client application 104 iscommunicatively coupled to other instances of the messaging clientapplication 104 and a messaging server system 108 via a network 106(e.g., the Internet).

Accordingly, each messaging client application 104 is able tocommunicate and exchange data with another messaging client application104 and with the messaging server system 108 via the network 106. Thedata exchanged between messaging client applications 104, and between amessaging client application 104 and the messaging server system 108,includes functions (e.g., commands to invoke functions) as well aspayload data (e.g., text, audio, video, or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality within either the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, and to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information, asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anapplication programming interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client devices 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., images or video) from a messaging client application 104 toa messaging server application 114 for possible access by anothermessaging client application 104; the setting of a collection of mediadata (e.g., a story); the retrieval of such collections; the retrievalof a list of friends of a user of a client device 102; the retrieval ofmessages and content; the adding and deletion of friends to and from asocial graph; the location of friends within the social graph; andopening application events (e.g., relating to the messaging clientapplication 104).

The application server 112 hosts a number of applications andsubsystems, including the messaging server application 114, an imageprocessing system 116, and a social network system 122. The messagingserver application 114 implements a number of message-processingtechnologies and functions particularly related to the aggregation andother processing of content (e.g., textual and multimedia content)included in messages received from multiple instances of the messagingclient application 104. As will be described in further detail, the textand media content from multiple sources may be aggregated intocollections of content (e.g., called stories or galleries). Thesecollections are then made available, by the messaging server application114, to the messaging client application 104. Other processor- andmemory-intensive processing of data may also be performed server-side bythe messaging server application 114, in view of the hardwarerequirements for such processing.

The application server 112 also includes the image processing system116, which is dedicated to performing various image processingoperations, typically with respect to images or video received withinthe payload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph (e.g., entity graph304 in FIG. 3) within the database 120. Examples of functions andservices supported by the social network system 122 include theidentification of other users of the messaging system 100 with whom aparticular user has relationships or whom the particular user is“following,” and also the identification of other entities and interestsof a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages processed by the messaging serverapplication 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100, according to example embodiments. Specifically,the messaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of subsystems, namely an ephemeral timer system 202, a collectionmanagement system 204, an annotation system 206, and an image masksystem 210.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message orcollection of messages (e.g., a SNAPCHAT Story), selectively display andenable access to messages and associated content via the messagingclient application 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media (e.g., collections of text, image, video, and audiodata). In some examples, a collection of content (e.g., messages,including images, video, text, and audio) may be organized into an“event gallery” or an “event story.” Such a collection may be madeavailable for a specified time period, such as the duration of an eventto which the content relates. For example, content relating to a musicconcert may be made available as a “story” for the duration of thatmusic concert. The collection management system 204 may also beresponsible for publishing an icon that provides notification of theexistence of a particular collection to the user interface of themessaging client application 104.

The collection management system 204 furthermore includes a curationinterface 208 that allows a collection manager to manage and curate aparticular collection of content. For example, the curation interface208 enables an event organizer to curate a collection of contentrelating to a specific event (e.g., delete inappropriate content orredundant messages). Additionally, the collection management system 204employs machine vision (or image recognition technology) and contentrules to automatically curate a content collection. In certainembodiments, compensation may be paid to a user for inclusion ofuser-generated content into a collection. In such cases, the curationinterface 208 operates to automatically make payments to such users forthe use of their content.

The annotation system 206 provides various functions that enable a userto annotate or otherwise modify or edit media content associated with amessage. For example, the annotation system 206 provides functionsrelated to the generation and publishing of media overlays for messagesprocessed by the messaging system 100. The annotation system 206operatively supplies a media overlay (e.g., a SNAPCHAT Geofilter orfilter) to the messaging client application 104 based on a geolocationof the client device 102. In another example, the annotation system 206operatively supplies a media overlay to the messaging client application104 based on other information, such as social network information ofthe user of the client device 102. A media overlay may include audio andvisual content and visual effects. Examples of audio and visual contentinclude pictures, text, logos, animations, and sound effects. An exampleof a visual effect includes color overlaying. The audio and visualcontent or the visual effects can be applied to a media content item(e.g., a photo) at the client device 102. For example, the media overlayincludes text that can be overlaid on top of a photograph generated bythe client device 102. In another example, the media overlay includes anidentification of a location (e.g., Venice Beach), a name of a liveevent, or a name of a merchant (e.g., Beach Coffee House). In anotherexample, the annotation system 206 uses the geolocation of the clientdevice 102 to identify a media overlay that includes the name of amerchant at the geolocation of the client device 102. The media overlaymay include other indicia associated with the merchant. The mediaoverlays may be stored in the database 120 and accessed through thedatabase server 118.

In one example embodiment, the annotation system 206 provides auser-based publication platform that enables users to select ageolocation on a map and upload content associated with the selectedgeolocation. The user may also specify circumstances under whichparticular content should be offered to other users. The annotationsystem 206 generates a media overlay that includes the uploaded contentand associates the uploaded content with the selected geolocation.

In another example embodiment, the annotation system 206 provides amerchant-based publication platform that enables merchants to select aparticular media overlay associated with a geolocation via a biddingprocess. For example, the annotation system 206 associates the mediaoverlay of a highest-bidding merchant with a corresponding geolocationfor a predefined amount of time.

The image mask system 210 manages generating image masks (e.g., pixelmasks) for images (e.g., images tracked in image table 308 discussedbelow). The image masks can be used by the messaging client application104 to produce visual effects on a depicted human subject. For example,the annotation system 206 can use the mask to apply different filters todifferent labeled areas using the mask.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain example embodiments. While the content of the database 120 isshown to comprise a number of tables, it will be appreciated that thedata could be stored in other types of data structures (e.g., as anobject-oriented database).

The database 120 includes message data stored within a message table314. An entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events, and so forth. Regardless of type, any entity regardingwhich the messaging server system 108 stores data may be a recognizedentity. Each entity is provided with a unique identifier, as well as anentity type identifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between or among entities. Suchrelationships may be social, professional (e.g., work at a commoncorporation or organization), interest-based, or activity-based, forexample.

The database 120 also stores annotation data, in the example form offilters, in an annotation table 312. Filters for which data is storedwithin the annotation table 312 are associated with and applied tovideos (for which data is stored in a video table 310) and/or images(for which data is stored in an image table 308). Filters, in oneexample, are overlays that are displayed as overlaid on an image orvideo during presentation to a recipient user. Filters may be of varioustypes, including user-selected filters from a gallery of filterspresented to a sending user by the messaging client application 104 whenthe sending user is composing a message. Other types of filters includegeolocation filters (also known as geo-filters), which may be presentedto a sending user based on geographic location. For example, geolocationfilters specific to a neighborhood or special location may be presentedwithin a user interface by the messaging client application 104, basedon geolocation information determined by a Global Positioning System(GPS) unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include a current temperature at a specificlocation, a current speed at which a sending user is traveling, abattery life for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe message table 314. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection (e.g., a SNAPCHAT Story or a gallery). The creation of aparticular collection may be initiated by a particular user (e.g., eachuser for whom a record is maintained in the entity table 302). A usermay create a “personal story” in the form of a collection of contentthat has been created and sent/broadcast by that user. To this end, theuser interface of the messaging client application 104 may include anicon that is user-selectable to enable a sending user to add specificcontent to his or her personal story.

A collection may also constitute a “live story,” which is a collectionof content from multiple users that is created manually, automatically,or using a combination of manual and automatic techniques. For example,a “live story” may constitute a curated stream of user-submitted contentfrom various locations and events. Users whose client devices 102 havelocation services enabled and are at a common location or event at aparticular time may, for example, be presented with an option, via auser interface of the messaging client application 104, to contributecontent to a particular live story. The live story may be identified tothe user by the messaging client application 104 based on his or herlocation. The end result is a “live story” told from a communityperspective.

A further type of content collection is known as a “location story,”which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102, and that is included        in the message 400.    -   A message image payload 406: image data captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data captured by a camera        component or retrieved from a memory component of the client        device 102, and that is included in the message 400.    -   A message audio payload 410: audio data captured by a microphone        or retrieved from the memory component of the client device 102,        and that is included in the message 400.    -   Message annotations 412: annotation data (e.g., filters,        stickers, or other enhancements) that represents annotations to        be applied to the message image payload 406, message video        payload 408, or message audio payload 410 of the message 400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, and message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, with each        of these parameter values being associated with respective        content items included in the content (e.g., a specific image in        the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifies values identifying        one or more content collections (e.g., “stories”) with which a        particular content item in the message image payload 406 of the        message 400 is associated. For example, multiple images within        the message image payload 406 may each be associated with        multiple content collections using identifier values.    -   A message tag 420: one or more tags, each of which is indicative        of the subject matter of content included in the message        payload. For example, where a particular image included in the        message image payload 406 depicts an animal (e.g., a lion), a        tag value may be included within the message tag 420 that is        indicative of the relevant animal. Tag values may be generated        manually, based on user input, or may be automatically generated        using, for example, image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 on        which the message 400 was generated and from which the message        400 was sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of the message 400may be pointers to locations in tables within which content data valuesare stored. For example, an image value in the message image payload 406may be a pointer to (or address of) a location within the image table308. Similarly, values within the message video payload 408 may point todata stored within the video table 310, values stored within the messageannotations 412 may point to data stored in the annotation table 312,values stored within the message story identifier 418 may point to datastored in the story table 306, and values stored within the messagesender identifier 422 and the message receiver identifier 424 may pointto user records stored within the entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to content (e.g., an ephemeral message502, and associated multimedia payload of data) or a content collection(e.g., an ephemeral message story 504) may be time-limited (e.g., madeephemeral).

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, where the messaging client application 104 is a SNAPCHATapplication client, an ephemeral message 502 is viewable by a receivinguser for up to a maximum of 10 seconds, depending on the amount of timethat the sending user specifies using the message duration parameter506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal SNAPCHAT Story, or anevent story). The ephemeral message story 504 has an associated storyduration parameter 508, a value of which determines a time duration forwhich the ephemeral message story 504 is presented and accessible tousers of the messaging system 100. The story duration parameter 508, forexample, may be the duration of a music concert, where the ephemeralmessage story 504 is a collection of content pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message 502 may “expire” andbecome inaccessible within the context of the ephemeral message story504, prior to the ephemeral message story 504 itself expiring in termsof the story duration parameter 508. The story duration parameter 508,story participation parameter 510, and message receiver identifier 424each provide input to a story timer 514, which operationally determineswhether a particular ephemeral message 502 of the ephemeral messagestory 504 will be displayed to a particular receiving user and, if so,for how long. Note that the ephemeral message story 504 is also aware ofthe identity of the particular receiving user as a result of the messagereceiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of the ephemeral message story 504, based on a storyparticipation parameter 510. Note that a message duration parameter 506may still determine the duration of time for which a particularephemeral message 502 is displayed to a receiving user, even within thecontext of the ephemeral message story 504. Accordingly, the messageduration parameter 506 determines the duration of time that a particularephemeral message 502 is displayed to a receiving user, regardless ofwhether the receiving user is viewing that ephemeral message 502 insideor outside the context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

In response to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (e.g., specifically, the messaging client application 104) to causean indicium (e.g., an icon) associated with the relevant ephemeralmessage story 504 to no longer be displayed within a user interface ofthe messaging client application 104. Similarly, when the ephemeraltimer system 202 determines that the message duration parameter 506 fora particular ephemeral message 502 has expired, the ephemeral timersystem 202 causes the messaging client application 104 to no longerdisplay an indicium (e.g., an icon or textual identification) associatedwith the ephemeral message 502.

FIG. 6 illustrates a block diagram showing example functional componentsprovided within the image mask system 210, according to someembodiments. In various example embodiments, the image mask system 210comprises a resizing engine 605, a segmentation engine 610, apost-processing engine 615, and a labeling engine 620. Briefly, theresizing engine 605 resizes input images, which the segmentation engine610 can use for classification. The segmentation engine 610 isconfigured to apply a convolutional neural network to an image toclassify pixels in the image. The post-processing engine 615 isconfigured to remove noise and enhance boundaries in a given image. Inparticular, for example, the post-processing engine 615 may use a guidedfilter, where for a given mask, M, the original image, I, from which themask was generated is used as a filter guide to smooth over errors inthe mask, M. The labeling engine 620 is configured to manage labeling ofthe training image data by receiving input (e.g., polygon data) from oneor more human labelers. The components themselves are communicativelycoupled (e.g., via appropriate interfaces) to each other and to variousdata sources, so as to allow information to be passed between theapplications or so as to allow the applications to share and accesscommon data.

FIG. 7 shows a flow diagram of a method 700 for implementing acomputationally efficient system for creating a mask, according to someexample embodiments. At operation 705, the resizing engine resizesimages to a plurality of sizes. For example, the resizing engine 605generates one or more enlarged images that are larger than the originalimage and further generates one or more reduced size images that aresmaller than the original image.

At operation 710, the segmentation engine 610 trains a machine learningscheme on training data. In some embodiments, the machine learningscheme is a convolutional neural network (CNN). Further, in some exampleembodiments, the segmentation engine 610 trains the convolutional neuralnetwork on one of the resized image sets based on the client device. Forexample, if the client device is a latest model smartphone with thelatest processing power, the segmentation engine 610 trains CNN on theenlarged image set; whereas if the client device is an older smartphonewith reduced processing power, the segmentation engine 610 trains theCNN on the reduced size image set.

At operation 715, the segmentation engine 610 identifies an input imagefor classification (e.g., area classification, mask generation). Atoperation 720, the semantic segmentation engine assigns a label to eachpixel of the image. The labels correspond to classes such as face,clothes, and headwear, as discussed above. At operation 725, thepost-processing engine 615 performs post processing on the output of thesegmentation engine 610 to refine boundaries between pixels havingdifferent labels. At operation 730, the post-processing engine 615performs further post-processing to remove noise. For example, thepost-processing engine 615 removes noise by removing the background soonly the foreground remains in the image (e.g., only the subject humanof the image and mask remain).

At operation 735, the post-processing engine 615 outputs the mask andimage data as output data. The output data can be used to perform moreaccurate live video filters on the subject being depicted in the image.For example, cartoon glasses may be overlaid on the subject's face usingthe face labeled pixels, or a cartoon hat may replace the real-life hat.

FIG. 8 shows a functional architecture 800 for processing labels,according to some example embodiments. Using multiple labels fortraining instead of just using foreground/background for trainingenables a machine learning scheme (e.g., a neural network) to learnricher semantics about the image, and further enables the machinelearning scheme in making more accurate predictions. In the exampleillustrated, the input image 805 is an image of a girl with a hatholding a wine glass. The semantic segmentation engine 610 applies itstrained machine learning scheme to the input image 805 to output labeldata 815 comprising the image with each pixel labeled or otherwiseassigned to a semantic class, such as hair, face, body, headwear, bodyskin, clothes, background, and so forth. The label data 815 is inputinto a post-processing engine 615 to perform additional refinements. Thepost-processing engine 615 outputs refined image data 825. Therefinements performed by the post-processing engine 615 refine theboundaries in the image (e.g., the boundary between hair pixels and facepixels) as discussed in further detail below. The refinements canfurther include removing noise data, e.g., the background, such that theresult only contains the foreground, e.g., the subject.

The segmentation engine 610 comprises multiple parameters that aretrained on manually labeled training data (e.g., images with areasmanually labeled as hair, manually labeled as face, etc.) For example,20,000 images may be manually labeled by one or more human labelers. Tomanually label an image, the human labeler draws a polygon for each ofthe objects (e.g., hair, face) in a given image. The labeling engine 620(discussed below) stores the coordinates of the vertices of the polygonas training data. In some example embodiments, each image is labeled bymultiple human labelers to ensure assignments of labels are consistent.After all images in the training set are labeled, the polygons are usedto generate a label mask for each labeled image where each pixel has avalue denoting the class of that pixel; for example, 0 denotesbackground, 1 denotes face, 2 denotes hair, and so forth, according tosome example embodiments.

FIG. 9 displays example architecture 900 for the segmentation engine610, according to some example embodiments. The segmentation engine 610may be implemented using a deep neural network (for example, a fullyconvolutional neural network). The convolutional neural network maycomprise multiple layers 905, including but not limited to a convolutionlayer, a pooling layer, a rectified-linear layer, and a deconvolutionallayer. The layers are stacked together, which means a given layerperforms operations on data output from the previous layer, startingfrom an input image 910. The outputs of each layer are feature maprepresentations in that they contain information spatially correspondingto the input image 910.

The convolution layer applies a bank of filters with learnableparameters to the output of the previous layer. The pooling layercomputes a maximum value or an average value for every local window ofthe previous features. The pooling layer reduces the spatial size of theprevious features. The rectified-linear layer thresholds values ofprevious features to be above zero. The rectified-linear layer acts asthe nonlinear operation in the neural network and can increase theexpressive power of the network. The deconvolution layer is one of thelast layers, according to some example embodiments, and is used toupscale the feature map to the size of input image 910. Since the sizeof an output data 915 of the deconvolution layer is the same as the sizeof the input image 910, the output data 915 can be used generate classscores for each pixel. The scores are further normalized using a SoftMaxfunction to represent the probability of each pixel belonging todifferent classes. In some implementations, there are multiple layers ofeach type of layer (e.g., multiple convolutional layers, multiplepooling layers, etc.) in one fully convolutional network in thesegmentation engine 610.

To efficiently execute from devices having constrained processing power,memory, and/or electrical power (e.g., a mobile device), the image masksystem 210 may integrate several approaches to improve efficiency. In afirst approach, the segmentation engine 610 only implements a singledeconvolution layer with a large upscale factor. Deconvolution layerscan be computationally inefficient (e.g., time-consuming) compared toother layers in a given network. Multiple deconvolution layers withsmaller upscale factors are slower than a single deconvolution layerwith a large upscale factor.

In a second approach, the first two layers in the plurality of layers905 down sample the image four times on each side. By using thisapproach, the layers that follow the first two only need to process 1/16of the input size, which increases the computational speed of thoselayers. Further, the prediction accuracy of the learned model in thesegmentation engine 610 is not affected by the downsampling approach.

In a third approach, the segmentation engine 610 implements one or morebottleneck modules, according to some example embodiments. An example ofa bottleneck module 1000 is illustrated in FIG. 10. The output of one ormore previous layers 1005 is first fed into 1×1 convolution layer 1010,which reduces the channels of the feature map output by the previouslayer 1005. The output from the 1×1 convolution layer 1010 is fed into a3×3 convolution layer 1015. The output from the 3×3 convolution layer1015 is fed into a 1×1 convolution layer 1020 to increase the channelsin the feature map. A summarizer 1025 (e.g., a skip-connection) thensums the output of 1×1 convolution layer 1020 with the feature map fromthe one or more previous layers 1005. In this way, the computationalrequirements are greatly reduced as compared to a normal 3×3 convolutionlayer.

In a fourth approach, as illustrated by the architecture 1100 in FIG.11, the images are pre-processed to different sizes to use as inputs forclient devices having different processing powers, according to someexample embodiments. The resizing engine 605 may resize one or moreinput images, e.g., image 1105, into different resolutions as part of amulti-scale training process for a multi-scale model (e.g., multi-scalemodel of a neural network). For example, the input images can be resizedinto a large size image set 1120 (e.g., images width of 96×160 pixels),a medium size image set 1115 (e.g., images with 64×112 pixels), and asmall size image set 1110 (e.g., images with 48×80 pixels). Thedifferent sized image sets can be used as training data to train theconvolutional neural network in the segmentation engine 610. Inparticular, as illustrated in FIG. 11, the large size image set 1120,the medium size image set 1115, and the small size image set 1110 areinput into the segmentation engine 610 to generate initial predictions,e.g., predicated image mask 1125. The initial predicated images can becompared against a ground truth image mask 1135 and the neural networkcan be trained through loss function adjustments 1130 (e.g., backpropagation) until the prediction more closely approximates the groundtruth images.

After the multi-scale training process is complete (e.g., at runtime),the segmentation engine 610 can operate from different types of clientdevices (e.g., iPhone® 7, iPhone® 6, iPhone® 4) and use the same modeleven though the client devices may use different resolutions.

In some example embodiments, each of the different sized training datais used to train different separate convolutional neural networks. Inthose example embodiments, the segmentation engine 610 identifies theclient device type and selects the appropriate model for the resolutionof the client device images. For example, if the client device is aniPhone® 7, the segmentation engine 610 uses the model trained on thelarge size image set 1120, whereas if the client device is an iPhone® 4,the segmentation engine 610 uses the model trained on the small sizeimage set 1110.

FIG. 12 shows an architecture 1200 and internal functional componentsfor the post-processing engine 615, according to some exampleembodiments. To reduce the potential jittering of the image segmentationmask, we apply a temporal smoothing over a series of consecutive images,as follows:Mask^((f)) _(average)=(1−coef_(temporal))·Mask^((t-1))_(average)+coef_(temporal)·Mask^((t))

Above, Mask^((t)) _(average) is the smoothed segmentation mask,coef_(temporal) is the temporal coefficient that can be set up by theapplication, and t represents the current frame. The output maskgenerated by the deep neural network (DNN) is a rough mask with a lowresolution. Direct use of the segmentation masks can lead to poorsegmentation effect due to the lack of details and edge information.

To overcome this issue, a post-processing pipeline is applied to refinethe segmentation mask and improve the quality of the final result. Thesteps of the post-processing pipeline include color space conversion,image erosion, guided filter, thresholding, and normalization, which areperformed by the block engines in FIG. 12. As an example, assume theoriginal input image is image 1205, and the segmentation engine 610 (atrained DNN) outputs initial mask 1210. In a typical use case onhigh-tier mobile device, image 1205 is a 3-channel RGB image with aresolution of 720×1280, while the initial mask 1210 is a grayscale imagewith a resolution of 96×160. The resolution can be different accordingto the computational capability of the devices to guarantee a betteruser experience by maintaining a reasonable frame rate.

The following steps of image processing are applied to improve the mask1210 generated by the segmentation engine 610 (e.g., the DNN in thesegmentation engine):

Use erode module 1215 to erode the initial mask 1215 to generate a newerode mask.

Use the downsample module 1230 to downsample image 1205 by a downscalefactor S_(downsample). For instance, if S_(dsample)=½, thenI_(downsample)=S_(downsample)·I (where I=image 1205);

Use color module 1235 to convert I_(downsample) into grayscale imageI_(gray);

In the upper pipeline, to match the size of the grayscale imageI_(gray), In a resize module 1220, the new erode mask (output by erodemodule 1215) is resized to generate a new resized mask image M_(resize);

Apply guided filter 1125 using I_(gray) and M_(resize) to generate a newmask, M_(gf), the guided filter may use image 1205 that corresponds to1210 to smooth over imperfections and errors in the mask 1210;

Use threshold and normalization module 1240 to improve the quality ofthe new mask M_(gf) by combining the pixel information from both newerode mask (output by erode module 1215) and M_(gf);

Use threshold and normalization module 1240 to apply thresholdingoperation on improved mask; and

Use threshold and normalization module 1240 to rescale the pixel valuesin the combined mask to achieve a smooth transition on the mask edges.

In some example embodiments, to reduce the effective computationrequirement, the above post-processing algorithm is applied on only on acropped area of an image. For example, as shown in the FIG. 13, thepost-processing engine 615 only implements post-processing in the area1305 (inside black frame) of portrait mask 1300. In this way, thepost-processing engine 615 is able to generate the resultant image data1310 while avoiding unnecessary computations involving the backgroundarea (e.g., areas outside the black frame).

FIG. 14 shows an example input image 1400 of a subject holding abeverage, according to some example embodiments. FIG. 14 further showsan example output image mask 1405 with each of the pixels of the subjectassigned to different classes. In particular, the hat is in a firstclass 1410 (denoted by right-leaning wide-space diagonal lines), thesubject's skin (face and hands) is in a second class 1415 (denoted by aleft-leaning thin-spaced diagonal lines), and the subject's dress is ina third class 1420 (denoted by left leaning wide-spaced diagonal lines).As illustrated in FIG. 14, the input image 1400 and the output imagemask 1405 are separate files that have similar pixel dimensions (heightand width). Each pixel of the input image 1400 may have three or morechannels to depict the subject (e.g., RGB channels); whereas each pixelof the image mask may have a pre-set value for a given area. Forexample, the pixels labeled by the first class 1410 (e.g., the pixelsthat depict the hat) may be given a value of 1, and the pixels labeledby the second class 1415 (e.g., the pixels that depict the subject'sskin) may be given a value of 2, and the pixels labeled by the firstclass 1420 (e.g., the pixels that depict the dress) may be given a valueof 3, and so on. Image editing algorithms, such as an algorithmconfigured to re-color the subject's skin to a false color, may applythe re-coloration to the original image 1400 referencing specific valuesof the areas contained in the image mask 1405. Although only three areasare shown (in three classes 1410, 1415, and 1420), it is appreciatedthat many labels may exist, each corresponding to a new class. Forexample, in some embodiments the face is labeled differently than thehands. In those example embodiments, the face may belong to a separateclass than the hands and be labeled as such.

According to some example embodiments, the segmentation engine 610 isfurther configured to apply the following guidelines:

1. Pixels corresponding to ears should be labeled as face (e.g., in theface class).

2. Pixels corresponding to masks, glasses, and goggles should be labeledas face.

3. Pixels corresponding to a beard or mustache should be labeled asface.

4. Pixels corresponding to headwear should be labeled as headwear.However, if the headwear is attached to a coat (e.g., a hood), then thecorresponding pixels should be labeled as clothes. A headscarf isheadwear and pixels corresponding to a headscarf should be labeled asheadwear even it extends to or over the body. Further, pixelscorresponding to an ordinary scarf should be labeled as clothes.

5. Pixels corresponding to earrings are labeled as face.

6. Pixels corresponding to visible skin on a subject's body are labeledas body skin.

7. Pixels corresponding to visible skin on neck should be labeled asbody skin.

8. Pixels corresponding to small gadgets on arms and hands, such aswatches, bracelets, and rings, are labeled as body skin.

9. Pixels corresponding to necklaces and neck bands are labeled as bodyskin if they are overlaid on body skin, but are labeled as clothes ifthey are overlaid on clothes.

10. For images with multiple persons: only label images with three orless major persons, ignore images with four or more people. Do not labelpixels corresponding to far away persons in the background of the image.

11. Pixels corresponding to hand-carried objects, e.g., bags, drinks,are considered background.

To further improve the frame rate in some low-tier client devices, thesegmentation engine 610 only applies the neural network to periodickeyframes (e.g., alternating frames of the original image sequence), andapplies a post-processing algorithm (e.g., guided filter) to the rest ofthe frames that were not processed using the neural network. In thisway, the image mask system 210 can generate usable segmentation masks ona client device with limited resources. For example, the segmentationengine 610 executes neural network to generate an image mask for everyNth frame of an image sequence. For the remaining frames, thepost-processing engine 615 applies a guided filter with the mask from apreceding frame (e.g., the preceding keyframe) and the current frame(e.g., a non-keyframe) to generate a mask for the current frame.Compared to the original algorithm, the speedup can be estimated asfollows:Speedup=(N*(t _(dnn) +t _(gf)))/(t _(dnn)+(N−1)*t _(gf))

Using the time measured on a high-tier client device (e.g., iPhone® 7)as an example, t_dnn=13 ms and t_gf=3 ms. Assume we run DNN on every 2frames. In this case, the resulting speedup is approximately 2×.

FIG. 15 is a block diagram illustrating an example software architecture1506, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 15 is a non-limiting example of asoftware architecture, and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1506 may execute on hardwaresuch as a machine 1600 of FIG. 16 that includes, among other things,processors, memory, and input/output (I/O) components. A representativehardware layer 1552 is illustrated and can represent, for example, themachine 1600 of FIG. 16. The representative hardware layer 1552 includesa processing unit 1554 having associated executable instructions 1504.The executable instructions 1504 represent the executable instructionsof the software architecture 1506, including implementation of themethods, components, and so forth described herein. The hardware layer1552 also includes a memory/storage 1556, which also has the executableinstructions 1504. The hardware layer 1552 may also comprise otherhardware 1558.

In the example architecture of FIG. 15, the software architecture 1506may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1506may include layers such as an operating system 1502, libraries 1520,frameworks/middleware 1518, applications 1516, and a presentation layer1514. Operationally, the applications 1516 and/or other componentswithin the layers may invoke API calls 1508 through the software stackand receive a response in the form of messages 1512. The layersillustrated are representative in nature and not all softwarearchitectures have all layers. For example, some mobile orspecial-purpose operating systems may not provide aframeworks/middleware 1518, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1502 may manage hardware resources and providecommon services. The operating system 1502 may include, for example, akernel 1522, services 1524, and drivers 1526. The kernel 1522 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1522 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1524 may provideother common services for the other software layers. The drivers 1526are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1526 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1520 provide a common infrastructure that is used by theapplications 1516 and/or other components and/or layers. The libraries1520 provide functionality that allows other software components toperform tasks in an easier fashion than by interfacing directly with theunderlying operating system 1502 functionality (e.g., kernel 1522,services 1524, and/or drivers 1526). The libraries 1520 may includesystem libraries 1544 (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematical functions, and the like. In addition, thelibraries 1520 may include API libraries 1546 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia formats such as MPEG4, H.264, MP3, AAC, AMR, JPG, or PNG),graphics libraries (e.g., an OpenGL framework that may be used to render2D and 3D graphic content on a display), database libraries (e.g.,SQLite that may provide various relational database functions), weblibraries (e.g., WebKit that may provide web browsing functionality),and the like. The libraries 1520 may also include a wide variety ofother libraries 1548 to provide many other APIs to the applications 1516and other software components/modules.

The frameworks/middleware 1518 provide a higher-level commoninfrastructure that may be used by the applications 1516 and/or othersoftware components/modules. For example, the frameworks/middleware 1518may provide various graphic user interface (GUI) functions, high-levelresource management, high-level location services, and so forth. Theframeworks/middleware 1518 may provide a broad spectrum of other APIsthat may be utilized by the applications 1516 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system 1502 or platform.

The applications 1516 include built-in applications 1538 and/orthird-party applications 1540. Examples of representative built-inapplications 1538 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. The third-party applications 1540 may includean application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 1540 may invoke the API calls 1508 provided bythe mobile operating system (such as the operating system 1502) tofacilitate functionality described herein.

The applications 1516 may use built-in operating system functions (e.g.,kernel 1522, services 1524, and/or drivers 1526), libraries 1520, andframeworks/middleware 1518 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such asthe presentation layer 1514. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 16 is a block diagram illustrating components of a machine 1600,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 16 shows a diagrammatic representation of the machine1600 in the example form of a computer system, within which instructions1616 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1600 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1616 may be used to implement modules or componentsdescribed herein. The instructions 1616 transform the general,non-programmed machine 1600 into a particular machine 1600 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1600 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1600 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1600 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smartphone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1616, sequentially or otherwise, that specify actions to betaken by the machine 1600. Further, while only a single machine 1600 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1616 to perform any one or more of the methodologiesdiscussed herein.

The machine 1600 may include processors 1610, memory/storage 1630, andI/O components 1650, which may be configured to communicate with eachother such as via a bus 1602. The memory/storage 1630 may include amemory 1632, such as a main memory, or other memory storage, and astorage unit 1636, both accessible to the processors 1610 such as viathe bus 1602. The storage unit 1636 and memory 1632 store theinstructions 1616 embodying any one or more of the methodologies orfunctions described herein. The instructions 1616 may also reside,completely or partially, within the memory 1632, within the storage unit1636, within at least one of the processors 1610 (e.g., within theprocessor cache memory accessible to processor units 1612 or 1614), orany suitable combination thereof, during execution thereof by themachine 1600. Accordingly, the memory 1632, the storage unit 1636, andthe memory of the processors 1610 are examples of machine-readablemedia.

The I/O components 1650 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1650 that are included in a particular machine 1600 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1650 may include many other components that are not shown inFIG. 16. The I/O components 1650 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various example embodiments, the I/O components 1650may include output components 1652 and input components 1654. The outputcomponents 1652 may include visual components (e.g., a display such as aplasma display panel (PDP), a light-emitting diode (LED) display, aliquid-crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1654 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstruments), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1650 may includebiometric components 1656, motion components 1658, environmentcomponents 1660, or position components 1662 among a wide array of othercomponents. For example, the biometric components 1656 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram-basedidentification), and the like. The motion components 1658 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 1660 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gassensors to detect concentrations of hazardous gases for safety or tomeasure pollutants in the atmosphere), or other components that mayprovide indications, measurements, or signals corresponding to asurrounding physical environment. The position components 1662 mayinclude location sensor components (e.g., a GPS receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1650 may include communication components 1664operable to couple the machine 1600 to a network 1680 or devices 1670via a coupling 1682 and a coupling 1672, respectively. For example, thecommunication components 1664 may include a network interface componentor other suitable device to interface with the network 1680. In furtherexamples, the communication components 1664 may include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), Wi-Fi® components,and other communication components to provide communication via othermodalities. The devices 1670 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 1664 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1664 may include radio frequency identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional barcodes such as Universal Product Code (UPC) barcode,multi-dimensional barcodes such as Quick Response (QR) code, Aztec code,Data Matrix, Dataglyph, MaxiCode, PDF418, Ultra Code, UCC RSS-2Dbarcode, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1664, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

GLOSSARY

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions 1616 forexecution by the machine 1600, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such instructions 1616. Instructions 1616 may betransmitted or received over the network 1680 using a transmissionmedium via a network interface device and using any one of a number ofwell-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine 1600 thatinterfaces to a communications network 1680 to obtain resources from oneor more server systems or other client devices 102. A client device 102may be, but is not limited to, a mobile phone, desktop computer, laptop,PDA, smartphone, tablet, ultrabook, netbook, multi-processor system,microprocessor-based or programmable consumer electronics system, gameconsole, set-top box, or any other communication device that a user mayuse to access a network 1680.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network 1680 that may be an ad hoc network, an intranet, anextranet, a virtual private network (VPN), a local area network (LAN), awireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network 1680 may include a wireless or cellular networkand the coupling may be a Code Division Multiple Access (CDMA)connection, a Global System for Mobile communications (GSM) connection,or another type of cellular or wireless coupling. In this example, thecoupling may implement any of a variety of types of data transfertechnology, such as Single Carrier Radio Transmission Technology(1×RTT), Evolution-Data Optimized (EVDO) technology, General PacketRadio Service (GPRS) technology, Enhanced Data rates for GSM Evolution(EDGE) technology, third Generation Partnership Project (3GPP) including3G, fourth generation wireless (4G) networks, Universal MobileTelecommunications System (UMTS), High-Speed Packet Access (HSPA),Worldwide Interoperability for Microwave Access (WiMAX), Long-TermEvolution (LTE) standard, others defined by various standard-settingorganizations, other long-range protocols, or other data transfertechnology.

“EPHEMERAL MESSAGE” in this context refers to a message 400 that isaccessible for a time-limited duration. An ephemeral message 502 may bea text, an image, a video, and the like. The access time for theephemeral message 502 may be set by the message sender. Alternatively,the access time may be a default setting or a setting specified by therecipient. Regardless of the setting technique, the message 400 istransitory.

“MACHINE-READABLE MEDIUM” in this context refers to a component, adevice, or other tangible media able to store instructions 1616 and datatemporarily or permanently and may include, but is not limited to,random-access memory (RAM), read-only memory (ROM), buffer memory, flashmemory, optical media, magnetic media, cache memory, other types ofstorage (e.g., erasable programmable read-only memory (EPROM)), and/orany suitable combination thereof. The term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions 1616. The term “machine-readable medium”shall also be taken to include any medium, or combination of multiplemedia, that is capable of storing instructions 1616 (e.g., code) forexecution by a machine 1600, such that the instructions 1616, whenexecuted by one or more processors 1610 of the machine 1600, cause themachine 1600 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

“COMPONENT” in this context refers to a device, a physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, APIs, or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor 1612 ora group of processors 1610) may be configured by software (e.g., anapplication or application portion) as a hardware component thatoperates to perform certain operations as described herein. A hardwarecomponent may also be implemented mechanically, electronically, or anysuitable combination thereof. For example, a hardware component mayinclude dedicated circuitry or logic that is permanently configured toperform certain operations. A hardware component may be aspecial-purpose processor, such as a field-programmable gate array(FPGA) or an application-specific integrated circuit (ASIC). A hardwarecomponent may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. Forexample, a hardware component may include software executed by ageneral-purpose processor or other programmable processor. Onceconfigured by such software, hardware components become specificmachines (or specific components of a machine 1600) uniquely tailored toperform the configured functions and are no longer general-purposeprocessors 1610. It will be appreciated that the decision to implement ahardware component mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.

Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processor 1612configured by software to become a special-purpose processor, thegeneral-purpose processor 1612 may be configured as respectivelydifferent special-purpose processors (e.g., comprising differenthardware components) at different times. Software accordingly configuresa particular processor 1612 or processors 1610, for example, toconstitute a particular hardware component at one instance of time andto constitute a different hardware component at a different instance oftime.

Hardware components can provide information to, and receive informationfrom, other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between or among suchhardware components may be achieved, for example, through the storageand retrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors 1610 that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors 1610 may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors1610. Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor 1612 or processors1610 being an example of hardware. For example, at least some of theoperations of a method may be performed by one or more processors 1610or processor-implemented components. Moreover, the one or moreprocessors 1610 may also operate to support performance of the relevantoperations in a “cloud computing” environment or as a “software as aservice” (SaaS). For example, at least some of the operations may beperformed by a group of computers (as examples of machines 1600including processors 1610), with these operations being accessible via anetwork 1680 (e.g., the Internet) and via one or more appropriateinterfaces (e.g., an API). The performance of certain of the operationsmay be distributed among the processors 1610, not only residing within asingle machine 1600, but deployed across a number of machines 1600. Insome example embodiments, the processors 1610 or processor-implementedcomponents may be located in a single geographic location (e.g., withina home environment, an office environment, or a server farm). In otherexample embodiments, the processors 1610 or processor-implementedcomponents may be distributed across a number of geographic locations.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor1612) that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,” etc.) and which producescorresponding output signals that are applied to operate a machine 1600.A processor may, for example, be a central processing unit (CPU), areduced instruction set computing (RISC) processor, a complexinstruction set computing (CISC) processor, a graphics processing unit(GPU), a digital signal processor (DSP), an ASIC, a radio-frequencyintegrated circuit (RFIC), or any combination thereof. A processor 1610may further be a multi-core processor 1610 having two or moreindependent processors 1612, 1614 (sometimes referred to as “cores”)that may execute instructions 1616 contemporaneously.

“TIMESTAMP” in this context refers to a sequence of characters orencoded information identifying when a certain event occurred, forexample giving date and time of day, sometimes accurate to a smallfraction of a second.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2017, SNAP INC., All Rights Reserved.

What is claimed is:
 1. A method comprising: generating, using one ormore processors of a user device, one or more images comprising aplurality of pixels; generating one or more reduced size images byreducing an initial image size of the one or more images to a reducedsize; generating image masks for the one or more images using a machinelearning scheme, the image masks assigning pixels from the one or moreimages to a plurality of segments, each segment from the plurality ofsegments describing a type of image feature area, the image masksgenerated by applying the machine learning scheme to the one or moreimages to generate initial image masks that are subsequently reduced insize to the reduced size to generate reduced size image masks, thereduced size image masks being refined by using the one or more reducedsize images as a guided filter, and rescaling the refined reduced sizeimage masks to a larger size to generate the image masks; generating oneor more modified images by applying a visual effect to pixels in the oneor more images that correspond to a specified segment from the pluralityof segments; and storing, by the one or more processors, the one or moremodified images in memory of the user device.
 2. The method of claim 1,wherein the one or more images is a plurality of images in a sequence,and the image masks a plurality of image masks in the same sequence. 3.The method of claim 1, further comprising: publishing the one or moremodified images on a network site as an ephemeral message.
 4. The methodof claim 1, wherein the one or more images are reduced in size by atleast half or more, the reduced size being at least one half smallerthan the initial image size.
 5. The method of claim 1, wherein theplurality of segments includes a first segment for a hair image featurearea, a second segment for a skin image feature area, and a thirdsegment for a clothes image feature area.
 6. The method of claim 1,wherein the visual effect is one or more of: a color modification, animage overlay, a scale transformation.
 7. The method of claim 1, whereinthe one or more images comprise a foreground and a background, whereinpixels that correspond to the background are not assigned to a segmentof the plurality of segments.
 8. The method of claim 1, wherein themachine learning scheme is a convolutional neural network comprising asingle deconvolutional layer.
 9. A system comprising: one or moreprocessors of a machine; and a memory storing instructions that, whenexecuted by the one or more processors, cause the machine to performoperations comprising: generating one or more images comprising aplurality of pixels; generating one or more reduced size images byreducing an initial image size of the one or more images to a reducedsize; generating image masks for the one or more images using a machinelearning scheme, the image masks assigning pixels from the one or moreimages to a plurality of segments, each segment from the plurality ofsegments describing a type of image feature area, the image masksgenerated by applying the machine learning scheme to the one or moreimages to generate initial image masks that are subsequently reduced insize to the reduced size to generate reduced size image masks, thereduced size image masks being refined by using the one or more reducedsize images as a guided filter, and rescaling the refined reduced sizeimage masks to a larger size to generate the image masks; generating oneor more modified images by applying a visual effect to pixels in the oneor more images that correspond to a specified segment from the pluralityof segments; and storing the one or more modified images.
 10. The systemof claim 9, wherein the one or more images is a plurality of images in asequence, and the image masks are a plurality of image masks in the samesequence.
 11. The system of claim 9, the operations further comprising:publishing the one or more modified images on a network site as anephemeral message.
 12. The system of claim 9, wherein the one or moreimages are reduced in size by at least half or more, the reduced sizebeing at least one half smaller than the initial image size.
 13. Thesystem of claim 9, wherein the plurality of segments includes a firstsegment for a hair image feature area, a second segment for a skin imagefeature area, and a third segment for a clothes image feature area. 14.The system of claim 9, wherein the visual effect is one or more of: acolor modification, an image overlay, a scale transformation.
 15. Thesystem of claim 9, wherein the one or more images comprise a foregroundand a background, wherein pixels that correspond to the background arenot assigned to a segment of the plurality of segments.
 16. The systemof claim 9, wherein the machine learning scheme is a convolutionalneural network comprising a single deconvolutional layer.
 17. Amachine-readable storage device embodying instructions that, whenexecuted by one or more processors of a machine, cause the machine toperform operations comprising: generating one or more images comprisinga plurality of pixels; generating one or more reduced size images byreducing an initial image size of the one or more images to a reducedsize; generating image masks for the one or more images using a machinelearning scheme, the image masks assigning pixels from the one or moreimages to a plurality of segments, each segment from the plurality ofsegments describing a type of image feature area, the image masksgenerated by applying the machine learning scheme to the one or moreimages to generate initial image masks that are subsequently reduced insize to the reduced size to generate reduced size image masks, thereduced size image masks being refined by using the one or more reducedsize images as a guided filter, and rescaling the refined reduced sizeimage masks to a larger size to generate the image masks; generating oneor more modified images by applying a visual effect to pixels in the oneor more images that correspond to a specified segment from the pluralityof segments; and storing the one or more modified images.
 18. Themachine-readable storage device of claim 17, wherein the one or moreimages is a plurality of images in a sequence, and the image masks are aplurality of image masks in the same sequence.
 19. The machine-readablestorage device of claim 17, the operations further comprising:publishing the one or more modified images on a network site as anephemeral message.
 20. The machine-readable storage device of claim 17,wherein the one or more images are reduced in size by at least half ormore, the reduced size being at least one half smaller than the initialimage size.